Reporting Human Capital Data

Data Equals Knowledge and Power

Executives are data hungry and data needy. And knowledge is power. However, human capital is one of the least measured and analyzed investments – a weak link in the chain of corporate expenditures. Ideally, when it comes to making important decisions, information about the workforce should be provided at the same level of sophistication as is the case with decisions about technology, money, products and services. The same level of rigor and surgical precision that applies to managing and reporting on financial processes should also be applied to human capital.

Unfortunately, many Human Resources (HR) and people management measurement systems are weak or non-existent. Without valid and reliable people measures, politics fills the gap! The quality of measurement and reporting on human capital generally lags well behind that of other key assets of the business. This measurement and reporting deficiency has been instrumental in keeping HR from their legitimate "seat at the table".

What Gets Measured Gets Managed

Human capital systems and measures should be used by management to optimize the value they create through effective investments in the management of employees. Without the right data, executives have a less than adequate basis for informed decision-making when managing their most vital intangible asset. The phrase that "people are our greatest asset" becomes empty rhetoric!

Measurement drives behavior. Opportunity now beckons for HR professionals to "step up to the plate" by providing better human capital measures and reporting. In an era of talent and skills shortages, measuring, diagnosing and reporting on key drivers of performance and retention are essential for:

people investment tracking

continuous improvement monitoring, and

managing risk

Characteristics of an Effective Measurement System

This human capital measurement system should incorporate the following characteristics:

a well-established empirical research underpinning

standardization

reliability

validity

forward-looking and predictive (of performance and retention), and

diagnostic (where appropriate)

The measurement system must be simple yet powerful, with the reporting component able to "speak the language of business" or show a clear linkage to the "bottom line".

The Key Measures of Human Capital

There is no universal agreement on the measurement and evaluation of human capital. Notwithstanding this lack of agreement, factors that consistently emerge from the empirical literature include employees':

knowledge and experience

affective commitment or emotional commitment to the organization (which is related to both performance and retention)

job satisfaction (which is related to both performance and retention)

discretionary effort (which is related to motivation and performance)

intention to stay (which is the main predictor of turnover)

turnover

skills/competencies (including both technical, leadership and "soft skills"), and

performance ratings

The Three Types of Human Capital Data

There are three categories of human capital data:

demographic

organizational process data, and

predictive data

Demographic data relates to the composition and structure of the workforce, including both:

This data constitutes "lag" indicators (e.g., turnover). It constitutes "coroner" measures or autopsy results by capturing the outcomes of what has been produced in the past. Focusing exclusively on such measures is akin to trying to win a game of football by watching the scoreboard! Past history doesn't always predict future outcomes. Whilst not denying the need to monitor and track continual improvement and outcomes, what is more critical is knowing the drivers or "levers to pull" to produce those outcomes.

Of the above three categories of data, the predictive/attitudinal data is the most important. It is called "physician" or predictive data and is a lead indicator of performance.

The People Data Cube: From Ad Hoc Data to an Integrated Platform

The three categories of human capital data (i.e., demographic, organizational process and predictive/attitudinal data) form the basis of a practical framework of appropriate workforce measures that otherwise transforms ad hoc or disjointed data into a more integrated platform for effective human capital reporting. The People Data Cube Model provides a conceptual framework for categorizing, analyzing and reporting on human capital.

Many organizations have a loose conglomeration of Human Resources initiatives, with little connection to each other, little alignment with business goals and little by way of measurement of bottom line impacts. This integrated platform unlocks the hidden value of data silos, creating business value. It enables the development of new and powerful insights into the workforce and the creation of business value.

Without the right data, executives have a less than adequate basis for informed investment decision-making concerning their most vital intangible asset. They may be spending excessively or inadequately on people-management interventions and initiatives, with risk factors unknown. Capturing and analyzing the three types of human capital data using an integrated model such as the People Data Cube gives organizations a powerful tool for maximizing the value of their human capital.

Colin Beames is a leading human capital, workforce trends, engagement and retention specialist. He has consulted to a broad spectrum of industry sectors, including both public and private organizations. Colin has worked with a number of organizations to assist them in adopting a more strategic approach to managing their workforce and developing their Human Capital Strategic Plans. He is a registered psychologist and holds a masters degree in Business Administration, an honors degree in Arts and an honors degree in Engineering. Colin may be contacted by email at
office@businessperform.com

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